{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b058ee87f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3TBGFw5c2FmgATbPx+d4gHJ5AXd2DzgTuwKRIi3ZMI5U6mlTq6MaW2trfEAw+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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b0599a6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "X,y=make_blobs(n_samples=50,centers=2,random_state=0,cluster_std=0.6)\n",
    "plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap='autumn')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
